code: def gaussian_df(data, mean=0, std=1):
      """
      Returns gaussian density for each data sample.
      Gaussian specified by the mean and standard deviation.
  
      Parameters
      ----------
      data : array-like
  
      mean : float
          Gaussian mean.
  
      std : float
          Gaussian standard deviation.
      """
      if std == 0:
          return np.where(data == mean, 1, 0)
  
      normalizer = 2 * np.pi * std**2
      return np.exp(-np.square(data - mean) / normalizer)
  
file_path: neupy/neupy/algorithms/competitive/neighbours.py
imports: from __future__ import division
  import numpy
  from neupy.core.docs import shared_docs
label_desc: Use image node_id 1 for calling a global function with example usage: gaussian_df(data, mean, std) and returns: np, np